Communications systems are used for transmission of information from one device to another. The devices included in the communications system typically have either a transmitter, a receiver, or both. Before transmission, information is encoded by a transmitter's encoder into a format suitable for transmission over a communications channel. The communications channel may be a transmission line or free space between the transmitter and the receiver. As the signal propagates through the channel, the transmitted signal is distorted by imperfections in the channel. Furthermore, the signal experiences degradation from noise interference picked up during transmission. An example of interference commonly encountered in bandlimited channels is called inter-symbol interference (ISI). ISI occurs as a result of the spreading of a transmitted symbol pulse due to the dispersive nature of the channel which results in an overlap of adjacent symbol pulses. At the receiver, the signal is decoded and translated into its original pre-encoded form. Both the transmitter and receiver are designed to minimize the effects of channel imperfections and interference. For the purposes of this disclosure, interference or distortion due to channel imperfections, or any combination thereof will be referred to generally as noise.
Various receiver designs can be implemented to compensate for noise caused by the transmitter and the channel. By way of example, an equalizer is a common choice for dealing with ISI. An equalizer may be implemented with a transversal filter, i.e. a delay line with T-second taps (where T is the symbol duration). The output of the taps are amplified and summed to generate a “soft estimate” of the transmitted symbol. The tap coefficients are set to subtract the interference from symbols that are adjacent in time to the desired symbols. Commonly, an adaptive equalization technique is employed whereby the coefficients are continually and automatically adjusted. The adaptive equalizer uses a prescribed algorithm, such as “least mean square” (LMS) or “recursive least square” (RLS) to estimate the tap coefficients. The “soft estimate” is coupled to a decision making device such as a channel decoder or a slicer. The decision making device applies a threshold operation in order to arrive at a “hard estimate” of the symbol transmitted from the transmitter.
The ability of a receiver to detect a signal in the presence of noise is based on the ratio of the received signal power and the noise power. This ratio is commonly known as the signal-to-noise power ratio (SNR), or the carrier-to-interference ratio (C/I). Industry usage of these terms, or similar terms, is often interchangeable, however, the meaning is the same. Accordingly, any reference to C/I herein will be understood by those skilled in the art to encompass the broad concept of measuring the effects of noise at various points in the communications system.
Typically, the C/I can be computed in the receiver by evaluating soft symbol estimates of a known transmitted symbol sequence. This can be accomplished in the receiver by computing the C/I for the transmitted pilot signal. Since the pilot signal is known, the receiver can compute the C/I based on the soft symbol estimates from the equalizer. The resultant C/I computation can be used for a number of purposes. In communications systems employing a variable rate data request scheme, the receiver can communicate to the transmitter the maximum data rate it can support based on the C/I. Furthermore, if the receiver includes a turbo decoder, then depending on the transmitted constellation, the log likelihood ratio (LLR) computation needs an accurate estimate of the C/I.
When choosing the tap coefficients of the equalizer based on the minimum mean square error criterion, e.g., LMS, RLS, the soft symbol estimates may include a bias in their amplitude. This bias impacts the accuracy of the slicer effectively reducing the received constellation relative to the desired constellation. As a result, system performance is typically worse for larger constellations, such as 16-QAM or 64-QAM constellations as compared to a 4-PSK constellation. Moreover, the severity of this bias increases as the C/I decreases. Accordingly, a C/I computation that accounts for this bias is desirable to improve the receiver's demodulation performance as well as provide a more accurate C/I estimation for supporting variable data rate request schemes and turbo decoding.